Unsupervised cross-modality domain adaptation is a challenging task in medical image analysis, and it becomes more challenging when source and target domain data are collected from multiple institutions. In this paper, we present our solution to tackle the multi-institutional unsupervised domain adaptation for the crossMoDA 2023 challenge. First, we perform unpaired image translation to translate the source domain images to the target domain, where we design a dynamic network to generate synthetic target domain images with controllable, site-specific styles. Afterwards, we train a segmentation model using the synthetic images and further reduce the domain gap by self-training. Our solution achieved the 1st place during both the validation and testing phases of the challenge. The code repository is publicly available at https://github.com/MedICL-VU/crossmoda2023.

翻译：无监督跨模态域适应是医学图像分析中的一项具有挑战性的任务，当源域和目标域数据来自多个机构时，这一任务变得更加困难。本文针对crossMoDA 2023挑战赛中的多机构无监督域适应问题提出了解决方案。首先，我们通过非配对图像翻译将源域图像转换至目标域，其中设计了一种动态网络来生成具有可控、特定站点风格的合成目标域图像。随后，利用合成图像训练分割模型，并通过自训练进一步缩小域间差距。该解决方案在挑战赛的验证与测试阶段均取得了第一名。代码仓库已公开于https://github.com/MedICL-VU/crossmoda2023。